Collaborating Authors

Opportunities in DARPA SubT Challenge


The DARPA Subterranean (SubT) Challenge aims to develop innovative technologies that would augment operations underground. The SubT Challenge allows teams to demonstrate new approaches for robotic systems to rapidly map, navigate, and search complex underground environments, including human-made tunnel systems, urban underground, and natural cave networks. The SubT Challenge is organized into two Competitions (Systems and Virtual), each with two tracks (DARPA-funded and self-funded). The Cave Circuit, the final of three Circuit events, is planned for later this year. Final Event, planned for summer of 2021, will put both Systems and Virtual teams to the test with courses that incorporate diverse elements from all three environments.

[P] 233 Colab Notebooks with NLP Models Found Here!


UPDATE - Super Duper NLP Repo 😎😎 Added another 52 notebooks bringing us to 233 total NLP Colabs. Thank you for contributing: Manu Romero,...

Statistics and Machine Learning Experiments in Poetry


This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, information energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.

Python for Machine Learning - Classes and Objects


This Python for Machine Learning Tutorial will help you learn the Python programming language from scratch. You'll learn about Classes and Objects in Python. Everything in this course is explained with the relevant example thus you will actually know how to implement the topics that you will learn in this course.

Recommender Systems from Learned Embeddings


We will use Movie ID and User ID to generate their corresponding embeddings. These embeddings are generated through the model training process along with other parameters. Once we have the embeddings, we build a K-Nearest Neighbor (KNN) model. Then whenever there is a user, we can get that user's embedding from our Neural Network model. We use this embedding to lookup in the KNN database and recommend top -- K movies to this user.

The start-up completes its first round of financing for 3 million dollars.


CINA head is a triage solution for neurovascular emergencies. This bundle offers automatic detection of intracranial hemorrhages (ICH), large vessel occlusion (LVO), and automatic estimation of ASPECT score. With an emergency triage system we allow radiologists to identify pathologies that require urgent care. The solution is based on CT-scan imaging. We use artificial intelligence to create algorithms that can determine the most urgent cases.

Enterprise Apps adopt AI in the Golden Age of AI


The demand for AI continues to increase according to forecasts by International Data Corporation. Enterprises will adopt AI in 2020 with an estimated 16% surge compared to previous years. Diversity is enabling the growth of AI as companies rely on AI for decision-making with bias incidents reducing according to the IDC report. The customer experience from AI is growing as enterprises analyze interactions, and respond to queries in real-time. Automated AI systems are offering customer support, an area humans have faced challenges because of physical limitations.

Can artificial intelligence understand human humor? - The Jerusalem Post


Can artificial intelligence understand human humor? According to Fei-Fei Li, professor in the Computer Science Department at Stanford University and co-director of Stanford's Human-Centered AI Institute, the answer is: not yet."Today's What kind of sentiment does it carry? Humor requires a deep and nuanced reasoning which is not a strength of current AI."A former Google VP and one of the world's expert in the field computer vision, in the talk Li highlighted how many Israeli researchers have impacted her over the course of her career."I It will need to happen in the future," she said.In the lecture, the professor focused on different projects to shape the future of artificial intelligence guaranteeing a more ethical approach, a goal that Zebra, a healthcare company proving AI-based medical image diagnosis, also shares.Together with tremendous opportunities, Li acknowledged how the new technologies developed risk to enhance problems such as a wider gap between generations in interacting with machines, but also job displacement, bias and privacy infringements."For this reason, we believe in a different approach to AI, a human-centered approach," she pointed out, explaining that the goal is to carry out research with a concern for its human impact, with the idea of augmenting people's capabilities rather than replacing them, as well as by drawing inspiration from human intelligence.

Bringing IBM NLP capabilities to the CORD-19 Dataset


To assist in the fight against the COVID-19 pandemic, prominent research institutes led by Allen Institute for AI (AI2) released earlier this year the COVID-19 Open Research Dataset (CORD-19). Comprised of scientific articles related to COVID-19, Sars-Cov-2, and related coronaviruses, the dataset (which at the time of writing this contains more than 75,000 full text scientific papers) is intended to mobilize researchers to apply recent advances in natural language processing to generate new insights in support of the fight against this infectious disease (1,2). While a tremendous resource, the dataset initially did not include information found in tables due to the difficulty of extracting tabular data. However, following the launch of the Kaggle challenge associated with CORD-19, table information rose to become the most requested feature by challenge participants. Recognizing that critical scientific facts and data are often organized in a tabular format, IBM Research AI offered to apply our extensive experience in document and table conversion to update the CORD-19 dataset and, in turn, open up additional critical information to the global science and medical community in efforts to fight COVID-19.

IT Decision Makers Will Increase Investments in Automation of COVID-19


Nearly two-thirds of US IT decision-makers plan to increase their investments in automation technology over the next year as a result of Covid-19, noting that intelligent self-service has been key to remaining agile during the crisis. These are some of the findings from a new survey conducted by market research firm Opinion Matters and commissioned by Intelligent Virtual Agent (IVA) platform provider Inference Solutions. The study, Intelligent Automation Post-Covid-19, explores how IT leaders in mid-sized businesses and enterprises across eight industries are evolving their digital strategies in the wake of the pandemic, and how they have used automation to tackle challenges driven by shutdowns and social distancing. Significantly, 71% of IT decision makers agree that intelligent self-service automation has helped their organizations remain agile, and 64% expect to increase automation investments over the coming year as a result of the crisis. More than a quarter (26%) of organizations will increase their investments by 10% or higher.